Investigation on Adaptive Denoising of Remote Sensing Image

Abstract

Abstract:

Remote sensing images are easily affected by noise in the process of acquisition and transmission. Based on the morphological component analysis (MCA) representation and the methods of inpainting to remote sensing images, the method of adaptive denoising on the basis of the MCA sparse decomposition and the method of denoising on the basis of image inpainting are both proposed. Compared with other classical denoising models, it is concluded that the former method can adaptivly remove the Gaussian white noise effectively, the latter method can adaptivly remove the salt and pepper noise of the gray or the colored remote sensing images effectively, especially can remove both salt noise and pepper noise at the same time. Both the subjective visual effects and the objective and quantitative evaluation of the methods are better than common models.

Key words: remote sensing images, sparse decomposition, image inpainting, image denoising

CLC Number:

TP391

ZHANG Ji-yao, ZHANG Xie, LIU Xiao, YI Wei-ning. Investigation on Adaptive Denoising of Remote Sensing Image[J]. Journal of Atmospheric and Environmental Optics, 2011, 6(5): 368-376.

References

[1] Gonzales. Digital Image Processing [M]. 2nd ed. Beijing: Publishing House of Electronics Industry, 2008: 175-224(in Chinese). 冈萨雷斯. 数字图像处理 [M]. 第二版.北京: 电子工业出版社, 2008: 175-224.
[2] Mallat S, Zhang Z. Matching pursuit with time frequency dictionaries [J]. IEEE Transactions on Signal Processing, 1993, 41(12): 3397-3415.
[3] Chen S S, Donoho D L, Saunders M A. Atomic decomposition by basis pursuit [J]. SIAM Journal on Scientific Computing, 1998, 20(1): 33-61.
[4] Bergeaud F, Mallat S G. Matching pursuit: adaptative representations of images and sounds [J]. Computational and Applied Mathematics, 1996, 15(2): 97-109.
[5] Pati Y C， Rezaiifar R, Krishnaprasad P S. Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition [C]. Proceedings of the 27th Annual Asilomar Conference in Signals, Systems, and Computers [M]. Los Alamitos: IEEE, 1993: 40-44.
[6] Elad M, Starck J L, Querre P. Simultaneous cartoon and texture image inpainting using morphological component analysis(MCA) [J]. Appl. Comput. Harmon. Anal., 2005, 19(3): 340-358. ?
[7] Bertalmio M, Sapiro G, Caselles V, et al. Image inpainting [C]. Proceedings of International Conference on Computer Graphics and Interactive Techniques [M]. New Orleans, Louisiana, USA, 2000: 417-424.
[8] Chan T F, Shen J H. Non-texture inpainting by curvature-driven diffusions (CDD) [J]. Journal of Visual Communication and Image Representation, 2001, 12(4): 436-449.
[9] Chan T F, Shen J H. Mathematical models for local non-texture inpainting [J]. SIAM Journal of Applied Mathematics, 2001, 62(3): 1019-1043.
[10] Zheng Mengqi. Inpainting Model, Algorithm and Their Research and Application [D]. Nanjing : Master’s Thesis of Nanjing University and Technology, 2005: 24-29(in Chinese). 郑孟琦. 图像修补模型、算法及其研究应用 [D]. 南京: 南京理工大学硕士论文, 2005: 24-29.
[11] Drori I, DanielC O, Hezy Y. Fragment based image completion [J]. ACM Transactions on Graphics, 2003, 22(3): 303-312.
[12] Criminisi A, Perez P, Toyama K. Region filling and object removal by exemplar-based image inpainting [J]. IEEE Transactions on Image Processing, 2004, 13(9): 1200-1212.
[13] Tang F, YingY T, Wang J, et al. A novel texture synthesis based algorithm for object removal in photographs [C]. Proceedings of Ninth Asian Computing Science Conference [M]. Chiang Mai, Thailand, 2004: 248-258.
[14] Criminisi A, Perez P, Toyama K. Object removal by exemplar-based inpainting [C]. Proceedings of IEEE Computer Society Conference on Computer Vision and Patter Recognition [M]. Monona Terrace Convention Center Madison, Wisconsin, USA, 2003, 2: 18-20.